The widespread integration of deep neural networks in developing data-driven surrogate models for high-fidelity simulations of complex physical systems highlights the critical necessity for robust uncertainty quantification techniques and credibility assessment methodologies, ensuring the reliable deployment of surrogate models in consequential decision-making. This study presents the Occam Plausibility Algorithm for surrogate models (OPAL-surrogate), providing a systematic framework to uncover predictive neural network-based surrogate models within the large space of potential models, including various neural network classes and choices of architecture and hyperparameters. The framework is grounded in hierarchical Bayesian inferences and employs model validation tests to evaluate the credibility and prediction reliability of the surrogate models under uncertainty. Leveraging these principles, OPAL-surrogate introduces a systematic and efficient strategy for balancing the trade-off between model complexity, accuracy, and prediction uncertainty. The effectiveness of OPAL-surrogate is demonstrated through two modeling problems, including the deformation of porous materials for building insulation and turbulent combustion flow for the ablation of solid fuels within hybrid rocket motors.

本研究介绍了基于Occam Plausibility Algorithm的代理模型(OPAL-surrogate)，提供了一个系统的框架来揭示大量潜在模型中基于深度神经网络的代理模型，包括不同的神经网络类和架构选择，并利用层次贝叶斯推理和模型验证测试来评估代理模型在不确定性下的可信度和预测可靠性，实现了模型复杂性、准确性和预测不确定性之间的平衡。通过两个建模问题，包括多孔材料变形用于建筑隔热和混合火箭马达中固体燃料剥蚀的湍流燃烧流，验证了OPAL-surrogate的有效性。

面对不确定性的可信神经网络代理模型战略发现框架